System and method of rotor management

ABSTRACT

In an aspect, a system comprising a computing device. The computing device is configured to determine a drag minimization axis of a rotor connected to an aircraft. The rotor includes a first end and a second end. The rotor is configured to rotate about an axis. The computing device is further configured to determine a halting point of the rotor, wherein the halting point includes a drag minimization axis of the rotor. The computing device is configured to send a halting command to at least a magnetic element to halt the rotor, wherein the halting process is configured to stop a movement of the rotor and position the rotor in the halting point. The position of the rotor in the halting point includes the first end pointing in one direction of the drag minimization axis and the second end pointing in an opposite direction of the first end.

FIELD OF THE INVENTION

The present invention generally relates to the field of rotor managementin aircraft. In particular, the present invention is directed to asystem and method for rotor management to reduce drag for an electricaircraft.

BACKGROUND

Modern aircraft, such as vertical landing and takeoff aircraft (VTOL)may include a set of rotors. These rotors may be stationary and not inuse during edgewise flight. As such, they may increase the airresistance and/or drag on the aircraft.

SUMMARY OF THE DISCLOSURE

In an aspect, a system comprising a computing device. The computingdevice is configured to determine a drag minimization axis of a rotorconnected to an aircraft. The rotor includes a first end and a secondend. The rotor is configured to rotate about an axis. The computingdevice is further configured to determine a halting point of the rotor,wherein the halting point includes a drag minimization axis of therotor. The computing device is configured to send a halting command toat least a magnetic element to halt the rotor, wherein the haltingprocess is configured to stop a movement of the rotor and position therotor in the halting point. The position of the rotor in the haltingpoint includes the first end pointing in one direction of the dragminimization axis and the second end pointing in an opposite directionof the first end.

In an aspect, a method comprising determining a drag minimization axisof a rotor connected to an aircraft. The rotor includes a first end anda second end. The rotor is configured to rotate about an axis. Themethod includes determining a halting point of the rotor. The haltingpoint includes a drag minimization axis of the rotor. The methodincludes sending a halting command to at least a magnetic element tohalt the rotor. The halting command is configured to stop a movement ofthe rotor and position the rotor in the halting point. The position ofthe rotor in the halting point includes the first end pointing in onedirection of the drag minimization axis and the second end pointing inan opposite direction of the first end.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a front view of an exemplary embodiment of an electricaircraft;

FIG. 2 is a block diagram of a system of rotor management;

FIG. 3 is a block diagram of an exemplary embodiment of a flightcontroller system;

FIG. 4 is a block diagram of an exemplary embodiment of a computingsystem;

FIG. 5 is an exemplary embodiment of a machine learning system; and

FIG. 6 is a flowchart of an exemplary embodiment of a method of rotormanagement.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. As used herein, the word “exemplary” or “illustrative” means“serving as an example, instance, or illustration.” Any implementationdescribed herein as “exemplary” or “illustrative” is not necessarily tobe construed as preferred or advantageous over other implementations.All of the implementations described below are exemplary implementationsprovided to enable persons skilled in the art to make or use theembodiments of the disclosure and are not intended to limit the scope ofthe disclosure, which is defined by the claims.

Described herein is a system including a computing device. In someembodiments, a computing device may be configured to determine a dragminimization axis of a rotor. In some embodiments, a rotor may beconnected to an aircraft. In some embodiments, a rotor may include afirst and a second end. In some embodiments, a rotor may be configuredto rotate about an axis. In some embodiments, a computing device may beconfigured to determine a halting point of a rotor. In some embodiments,determining a halting point may include determining if a rotor is in astable position once the rotor has halted. A halting point may include adrag minimization axis of a rotor. In some embodiments, a computingdevice may be configured to send a halting command to at least amagnetic element. A halting command may include a command to halt arotor. In some embodiments, a halting command may include a command tostop a movement of a rotor. In some embodiments, a halting command mayinclude a command to position a rotor in a halting point. In someembodiments, a position of a rotor in a halting point may include afirst end of the rotor pointing in one direction of a drag minimizationaxis and a second end of the rotor pointing in an opposite direction ofthe first end. In some embodiments, determining a halting point of arotor may include determining if the rotor is in a stable position oncethe rotor has halted. In some embodiments, if a rotor is determined notto be in a stable position, a computing device may be configured to senda command to at least a magnetic element to rotate the rotor in adirection of rotation until the rotor is in a stable position. In someembodiments, a computing device may be configured to detect a torque ofa rotor and send a command to at least a magnetic element to apply a netzero torque to the rotor. In some embodiments, a computing device may beconfigured to determine a starting point of a rotor. In someembodiments, a computing device may be configured to determine arotational angle of a rotor. In some embodiments, a computing device maybe configured to send a command to at least a magnetic element to rotatea rotor to a starting point based on a rotational angle of the rotor. Insome embodiments, a starting point of a rotor may include a point of therotor to continue rotating after being halted. In some embodiments, acomputing device may be configured to continuously update a haltingpoint of a rotor based on a feedback from a sensor of the rotor. In someembodiments, a computing device may be configured to utilize a machinelearning model to determine an optimal halting point. In someembodiments, a computing device may be configured to determine astarting point of a rotor while the rotor is in a halted position. Insome embodiments, a computing device may be configured to determine atorque threshold. In some embodiments, a torque threshold may include anamount of torque needed to rotate a rotor at a specific rate ofrotation. In some embodiments, a halting command may be configured tostop a movement of a rotor during a flight of an aircraft.

Described herein is a method including determining a drag minimizationaxis of a rotor. In some embodiments, a rotor may be connected to anaircraft. In some embodiments, a rotor may include a first end and asecond end. In some embodiments, a rotor may be configured to rotateabout an axis. In some embodiments, a method includes determining ahalting point of a rotor. A halting point may include a dragminimization axis of a rotor. In some embodiments, a method includessending a halting command to at least a magnetic element to halt arotor. In some embodiments, a halting command may be configured to stopa movement of a rotor and position the rotor in a halting point. In someembodiments, a position of a rotor in a halting point may include afirst end of a rotor pointing in one direction of a drag minimizationaxis and a second end of the rotor pointing in an opposite direction ofthe first end. In some embodiments, determining a halting point of arotor may include determining if the rotor is in a stable position oncethe rotor has halted. In some embodiments, if a rotor is determined notto be in a stable position, a computing device may be configured to senda command to at least a magnetic element to rotate the rotor in adirection of rotating until the rotor is in a stable position. In someembodiments, a computing device may be configured to detect a torque ofa rotor and send a command to a plurality of magnets to apply a net zerotorque to the rotor. In some embodiments, a computing device may beconfigured to determine a starting point of a rotor. In someembodiments, a computing device may be configured to determine arotational angle of a rotor. In some embodiments, a computing device maybe configured to send a command to at least a magnetic element to rotatea rotor to a starting point based on a rotational angle of the rotor. Insome embodiments, a starting point of a rotor may include a point of therotor to continue rotating after being halted. In some embodiments, acomputing device may be configured to continuously update a haltingpoint of a rotor based on a feedback from a sensor of the rotor. In someembodiments, a computing device may be configured to utilize a machinelearning model to determine an optimal halting point. In someembodiments, a computing device may be configured to determine astarting point of a rotor while the rotor is in a halted position. Insome embodiments, a computing device may be configured to determine atorque threshold. In some embodiments, a torque threshold may include anamount of torque needed to rotate a rotor at a specific rate ofrotation. In some embodiments, a halting command may be configured tostop a movement of a rotor during a flight of an aircraft.

Referring now to FIG. 1 , an illustration of an exemplary embodiment ofan aircraft 100 is shown. In some embodiments, aircraft 100 may includean electric aircraft. In some embodiments, aircraft 100 may include avertical takeoff and landing aircraft (eVTOL). As used herein, avertical take-off and landing (eVTOL) aircraft is one that may hover,take off, and land vertically. An eVTOL, as used herein, is anelectrically powered aircraft typically using an energy source, of aplurality of energy sources to power the aircraft. In order to optimizethe power and energy necessary to propel the aircraft. eVTOL may becapable of rotor-based cruising flight, rotor-based takeoff, rotor-basedlanding, fixed-wing cruising flight, airplane-style takeoff,airplane-style landing, and/or any combination thereof. Rotor-basedflight, as described herein, is where the aircraft generated lift andpropulsion by way of one or more powered rotors coupled with an engine,such as a “quad copter,” multi-rotor helicopter, or other vehicle thatmaintains its lift primarily using downward thrusting propulsors.Fixed-wing flight, as described herein, is where the aircraft is capableof flight using wings and/or foils that generate life caused by theaircraft's forward airspeed and the shape of the wings and/or foils,such as airplane-style flight.

With continued reference to FIG. 1 , a number of aerodynamic forces mayact upon the electric aircraft 100 during flight. Forces acting on anaircraft 100 during flight may include, without limitation, thrust, theforward force produced by the rotating element of the aircraft 100 andacts parallel to the longitudinal axis. Another force acting uponaircraft 100 may be, without limitation, drag, which may be defined as arearward retarding force which is caused by disruption of airflow by anyprotruding surface of the aircraft 100 such as, without limitation, thewing, rotor, and fuselage. Drag may oppose thrust and acts rearwardparallel to the relative wind. A further force acting upon aircraft 100may include, without limitation, weight, which may include a combinedload of the electric aircraft 100 itself, crew, baggage, and/or fuel.Weight may pull aircraft 100 downward due to the force of gravity. Anadditional force acting on aircraft 100 may include, without limitation,lift, which may act to oppose the downward force of weight and may beproduced by the dynamic effect of air acting on the airfoil and/ordownward thrust from the propulsor of the electric aircraft. Liftgenerated by the airfoil may depend on speed of airflow, density of air,total area of an airfoil and/or segment thereof, and/or an angle ofattack between air and the airfoil. For example, and without limitation,aircraft 100 are designed to be as lightweight as possible. Reducing theweight of the aircraft and designing to reduce the number of componentsis essential to optimize the weight. To save energy, it may be useful toreduce weight of components of an aircraft 100, including withoutlimitation propulsors and/or propulsion assemblies. In an embodiment,the motor may eliminate need for many external structural features thatotherwise might be needed to join one component to another component.The motor may also increase energy efficiency by enabling a lowerphysical propulsor profile, reducing drag and/or wind resistance. Thismay also increase durability by lessening the extent to which dragand/or wind resistance add to forces acting on aircraft 100 and/orpropulsors.

Referring still to FIG. 1 , aircraft 100 may include at least a verticalpropulsor 104 and at least a forward propulsor 108. A forward propulsoris a propulsor that propels the aircraft in a forward direction. Forwardin this context is not an indication of the propulsor position on theaircraft; one or more propulsors mounted on the front, on the wings, atthe rear, etc. A vertical propulsor is a propulsor that propels theaircraft in an upward direction; one of more vertical propulsors may bemounted on the front, on the wings, at the rear, and/or any suitablelocation. A propulsor, as used herein, is a component or device used topropel a craft by exerting force on a fluid medium, which may include agaseous medium such as air or a liquid medium such as water. At least avertical propulsor 104 is a propulsor that generates a substantiallydownward thrust, tending to propel an aircraft in a vertical directionproviding thrust for maneuvers such as without limitation, verticaltake-off, vertical landing, hovering, and/or rotor-based flight such as“quadcopter” or similar styles of flight.

With continued reference to FIG. 1 , at least a forward propulsor 108 asused in this disclosure is a propulsor positioned for propelling anaircraft in a “forward” direction; at least a forward propulsor mayinclude one or more propulsors mounted on the front, on the wings, atthe rear, or a combination of any such positions. At least a forwardpropulsor may propel an aircraft forward for fixed-wing and/or“airplane”-style flight, takeoff, and/or landing, and/or may propel theaircraft forward or backward on the ground. At least a verticalpropulsor 104 and at least a forward propulsor 108 includes a thrustelement. At least a thrust element may include any device or componentthat converts the mechanical energy of a motor, for instance in the formof rotational motion of a shaft, into thrust in a fluid medium. At leasta thrust element may include, without limitation, a device using movingor rotating foils, including without limitation one or more rotors, anairscrew or propeller, a set of airscrews or propellers such ascontrarotating propellers, a moving or flapping wing, or the like. Atleast a thrust element may include without limitation a marine propelleror screw, an impeller, a turbine, a pump-jet, a paddle or paddle-baseddevice, or the like. As another non-limiting example, at least a thrustelement may include an eight-bladed pusher propeller, such as aneight-bladed propeller mounted behind the engine to ensure the driveshaft is in compression. Propulsors may include at least a motormechanically coupled to the at least a first propulsor as a source ofthrust. A motor may include without limitation, any electric motor,where an electric motor is a device that converts electrical energy intomechanical energy, for instance by causing a shaft to rotate. At least amotor may be driven by direct current (DC) electric power; for instance,at least a first motor may include a brushed DC at least a first motor,or the like. At least a first motor may be driven by electric powerhaving varying or reversing voltage levels, such as alternating current(AC) power as produced by an alternating current generator and/orinverter, or otherwise varying power, such as produced by a switchingpower source. At least a first motor may include, without limitation,brushless DC electric motors, permanent magnet synchronous at least afirst motor, switched reluctance motors, or induction motors. Inaddition to inverter and/or a switching power source, a circuit drivingat least a first motor may include electronic speed controllers or othercomponents for regulating motor speed, rotation direction, and/ordynamic braking. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various devices that may be used asat least a thrust element.

With continued reference to FIG. 1 , during flight, a number of forcesmay act upon the electric aircraft. Forces acting on an aircraft 100during flight may include thrust, the forward force produced by therotating element of the aircraft 100 and acts parallel to thelongitudinal axis. Drag may be defined as a rearward retarding forcewhich is caused by disruption of airflow by any protruding surface ofthe aircraft 100 such as, without limitation, the wing, rotor, andfuselage. Drag may oppose thrust and acts rearward parallel to therelative wind. Another force acting on aircraft 100 may include weight,which may include a combined load of the aircraft 100 itself, crew,baggage and fuel. Weight may pull aircraft 100 downward due to the forceof gravity. An additional force acting on aircraft 100 may include lift,which may act to oppose the downward force of weight and may be producedby the dynamic effect of air acting on the airfoil and/or downwardthrust from at least a propulsor. Lift generated by the airfoil maydepends on speed of airflow, density of air, total area of an airfoiland/or segment thereof, and/or an angle of attack between air and theairfoil.

Referring now to FIG. 2 , an exemplary embodiment of a rotor managementsystem 200 is illustrated. System 200 may include an aircraft 208. Insome embodiments, aircraft 208 may be an eVTOL. In some embodiments,aircraft 208 may have one or more states of operation. Aircraft 208 mayhave a hover state. In a hover state, aircraft 208 may be moving throughthe air along a vertical path. In some embodiments, aircraft 208 may bein a hover state during liftoff operations. In another embodiment,aircraft 208 may be in a hover state in landing operations. In otherembodiments, a hover state may be when aircraft 208 maintains analtitude when airborne. Aircraft 208 may use rotors 216 to achieveascent and descent in a hover state. In some embodiments, aircraft 208may have a fixed-wing flight state. Aircraft 208 may be in a fixed-wingflight state during forward, backward, and sideways propulsion. Afixed-wing flight state may include edgewise flight. In someembodiments, aircraft 208 may have a first set of rotors for a hoverstate. In other embodiments, aircraft 208 may have a second set ofrotors for a fixed-wing flight state. In some embodiments, aircraft 208may use the same set of propulsors for both hover state and fixed-wingflight states.

In some embodiments, and with continued reference to FIG. 2 , system 200may include a computing device 204. In some embodiments, computingdevice 204 may include a flight controller. In some embodiments, aflight controller may be as described below with reference to FIG. 4 .

In some embodiments, computing device 204 may have a sensor. In someembodiments, computing device 204 may be configured to detect aplurality of flight operations of aircraft 208. In some embodiments,computing device 204 may detect a change of aircraft 208 during atransition of aircraft 208 between a fixed-wing flight state and a hoverstate. In some embodiments, system 200 may have at least a magneticelement 212.

With continued reference to FIG. 2 , a at least a magnetic element 212may be configured to operably move rotor 116. At least a magneticelement 212 may be coupled to rotor 216. In some embodiments, rotor 116and movement thereof may be as described in U.S. patent application Ser.No. 16/938,952, filed Jul. 25, 2020, titled “INTEGRATED ELECTRICPROPULSION ASSEMBLY”, of which is incorporated by reference herein inits entirety. In some embodiments, at least a magnetic element 212 mayinclude a plurality of components configured to apply a torque to rotor216. In some embodiments, at least a magnetic element 212 may include anelement that may generate a magnetic field. For example, at least amagnetic element 212 may include one or more magnets which may beassembled in rows along a structural casing component. Further, at leasta magnetic element 212 may include one or more magnets having magneticpoles oriented in at least a first direction. One or more magnets mayinclude at least a permanent magnet. Permanent magnets may be composedof, but are not limited to, ceramic, alnico, samarium cobalt, neodymiumiron boron materials, any rare earth magnets, and the like. Further, oneor more magnets may include an electromagnet. As used herein, anelectromagnet is an electrical component that generates magnetic fieldvia induction. An electromagnet may include a coil of electricallyconducting material, through which an electric current flow to generatethe magnetic field, also called a field coil of field winding. A coilmay be wound around a magnetic core, which may include withoutlimitation an iron core or other magnetic material. A core may include aplurality of steel rings insulated from one another and then laminatedtogether. A plurality of steel rings may include slots in which theconducting wire will wrap around to form a coil. at least a magneticelement 212 may act to produce or generate a magnetic field to causeother magnetic elements to rotate, as described in further detail below.Rotor 216 may include a frame to house components including at least amagnetic element 212, as well as one or more other elements orcomponents. In an embodiment, a magnetic field may be generated by atleast a magnetic element 212 and may include a variable magnetic field.In embodiments, a variable magnetic field may be achieved by use of aninverter, a controller, or the like. In some embodiments, a at least amagnetic element 212 may include a plurality of inverters that may beconfigured to transform DC power to AC power. The AC power may be usedto drive the rotor by adjusting the frequency and voltage supplied tothe rotor. In some embodiments, a plurality of inverters may beconfigured to output between 100 and 300 kwh of electrical power torotor 216. In some embodiments, a plurality of inverters may beconfigured to output 200 kwh of electrical power to rotor 216. Aninverter may be entirely electronic or a combination of mechanicalelements and electronic circuitry. An invertor may allow for variablespeed and torque of rotor 216 based on the demands of the aircraft 208.In some embodiments, inverters of a plurality of inverters may include aplurality of wires. A plurality of wires may be wound around one or morestators of rotor 216. A plurality of wires may have multiple windingsaround one or more stators of rotor 216. In some embodiments, eachwinding of the plurality of wires may be connected to a differentinverter. Computing device 204 may be configured to communicate data toand from the at least a magnetic element 212. In some embodiments,computing device 204 may communicate data to and from the at least amagnetic element 212 wirelessly. In other embodiments, computing device204 may communicate data to and from at least a magnetic element 212 viaa wired connection. In some embodiments, computing device 204 may beconfigured to send commands to at least a magnetic element 212. In someembodiments, computing device 204 may send a command to at least amagnetic element 212 to apply a torque to rotor 216. In someembodiments, at least a magnetic element 212 may be configured to applybetween 10 and 30 newton meters of torque to rotor 216. In someembodiments, at least a magnetic element 212 may be configured to applya torque of 22 newton meters to rotor 216.

In some embodiments, and with continued reference to FIG. 2 , computingdevice 204 may send a command to at least a magnetic element 212 toapply a clockwise torque to rotor 216. In other embodiments, computingdevice 204 may send a command to at least a magnetic element 212 toapply a counter-clockwise torque to rotor 216. In some embodiments,computing device 204 may send a command to at least a magnetic element212 to apply zero torque to rotor 216.

In some embodiments, and still referencing FIG. 2 , rotor managementsystem 200 may have a second set of magnetic elements. Computing device204 may send a command to a second set of magnetic elements to keeprotor 216 in a stationary position. In some embodiments, computingdevice 204 may send commands to at least a magnetic element 212 to moverotor 216 within a tolerance range of parking. In some embodiments, atolerance range may be +2 degrees. In other embodiments, a tolerancerange may be −2 degrees. In yet other embodiments, a tolerance range maybe between −10 degrees to +10 degrees. In other embodiments, a tolerancemay be greater than or less than between −10 degrees to +10 degrees. Insome embodiments, a tolerance range may be determined based on batterylevels of aircraft 208. In other embodiments, a tolerance range may bedetermined based on heating caused by a parking and unparking of rotor216. Computing device 204 may detect a transition of aircraft 208between a hover state and a fixed-wing flight state. Computing device204 may send a plurality of commands to at least a magnetic element 212.In some embodiments, computing device 204 may detect aircraft 208 in ahover state. Computing device 204 may send a command to at least amagnetic element 212 to position rotor 216 to aid aircraft 208 in ahover state. Computing device 204 may send a command to at least a firstset of inverters 210 to allow free rotation of rotor 216. In a hoverstate, rotor 216 may generate lift to move aircraft 208 along a verticalpath. Computing device 204 may detect aircraft 208 in a fixed-wingflight state. In some embodiments, in a fixed-wing flight state, acommand to position rotor 216 to reduce air resistance of aircraft 208may be sent to at least a magnetic element 212. Rotor 216 may be alignedalong a longitudinal axis of aircraft 208. In other embodiments,computing device 204 may determine a minimal drag axis of electricaircraft 208. In some embodiments, the minimal drag axis may align witha distal end of a first end 228 of rotor 216 to a distal end of a secondend 232 of rotor 216. In other embodiments, computing device 204 maydetermine a minimal drag axis based on surrounding airflow of aircraft208. A drag minimization axis and determining thereof may be asdescribed in U.S. patent application Ser. No. 17/362,454 filed Jun. 29,2021, titled “METHOD OF PROPULSOR MANAGEMENT IN ELECTRIC AIRCRAFT”, ofwhich is incorporated herein by reference in its entirety. Computingdevice 204 may send a command to at least a magnetic element 212 toposition rotor 216 to point towards a direction of surrounding airflowin order to reduce drag.

In some embodiments, and with continued reference to FIG. 2 , rotor 216may be configured to be in a halting point 220. A “halting point” asused in this disclosure is a physical position that a moving rotor is inwhen the moving rotor comes to a complete stop. Halting point 220 mayinclude a position in which rotor 216 may be halted or otherwise slowedto a complete stop. In some embodiments, halting point 220 may include apoint of minimum drag across rotor 216. Halting point 220 may beexpressed in terms of a time, such as, but not limited to, a time atwhich to start applying a negative torque to rotor 216. In someembodiments, halting point 220 may be expressed in terms of an angle,such as, but not limited to, an angle in advance of a desired haltedposition at which to begin applying negative torque to rotor 216. Insome embodiments, halting point 220 may be configured to include aposition in which first end 228 of rotor 216 and second end 232 of rotor216 may be aligned across a minimum drag axis. In some embodiments,first end 228 of rotor 216 may have a greater length than second end 232of rotor 216. First end 228 and second end 232 of rotor 216 may beconfigured to include differing lengths that may reduce air drag inhalting point 220 during a forward movement of aircraft 208, such as atransition from a hover state to a fixed-wing flight state.

Continuing to reference FIG. 2 , in some embodiments system 200 mayinclude a zero crossing indicator. In some embodiments, a zero crossingindicator can include a reflective patch. A zero crossing indicator maybe placed beneath rotor 216. A zero crossing indicator may be configuredto detect when rotor 216 is in halting point 220. A zero crossingindicator may be configured to detect a rotational angle of rotor 216.In some embodiments, a zero crossing indicator may be configured todetect a rotational speed of rotor 216. In some embodiments, a zerocrossing indicator may be configured to detect a torque of rotor 216.Computing device 204 may be configured to communicate with a zerocrossing indicator. Computing device 204 may be configured to send acommand to at least a magnetic element 212 based on data received from azero crossing indicator. A command sent to at least a magnetic element212 may include a command to apply a torque to rotor 216. A feedbackloop may be implemented where computing device 204 may continuouslyadjust how much torque is applied from at least a magnetic element 212to rotor 216. At least a magnetic element 212 may be configured toadjust and/or control a movement of rotor 216.

Continuing to reference FIG. 2 , system 200 may include a plurality ofsensing devices, such as, but not limited to, accelerometers,gyroscopes, inertial measurement unit (IMU) and the like. In someembodiments, a plurality of sensing devices may be configured to be incommunication with computing device 204. Computing device 204 may beable to measure an acceleration and/or angular rate of rotor 216 througha plurality of sensing devices. In some embodiments, rotor 216 mayinclude a plurality of laser. Rotor 216 may include one or more lasersthat may be configured to point downward towards aircraft 208 so as totrace a circle during a movement of rotor 216. In some embodiments,system 200 may include laser sensors that may be configured to detect aplurality of lasers emitting from rotor 216. In some embodiments, arotational angle may be determined by the plurality of laser sensors.

In some embodiments, and continuing to refer to FIG. 2 , computingdevice 204 may be configured to calculate a halting point 220. Computingdevice 204 may calculate halting point 220 while rotor 216 is in motion.Computing device 204 may calculate halting point 220 continuously and/orin real-time. Computing device 204 may detect a time or angle at whichto begin halting rotor 216. Computing device 204 may be configured tolook for a starting time and/or angle at which to begin halting rotor216. In some embodiments, a specific time and/or angle may include atriggering point. At a triggering point, computing device 204 may beconfigured to begin a halting of rotor 216. Halting of rotor 216 mayinclude computing device 204 sending a command to at least a magneticelement 212 to apply a torque to rotor 216. Computing device 204 may beconfigured to detect an unstable position of rotor 216 in halting point220. In some embodiments, computing device 204 may start a positionstability check of rotor 216 once rotor 216 has stopped and an angularspeed of rotor 216 is zero. A stable position may include a position inwhich first end 228 of rotor 216 and second end 232 of rotor 216 arewithin a tilt angle. A tilt angle may include a range of acceptableangles in which first end 228 of rotor 216 and second end 232 of rotor216 may be offset from a horizontal axis and/or level plane. If firstend 228 and second end 232 of rotor 216 are not within an acceptablerange of tilt angles, computing device 204 determines rotor 216 is in anunstable position. Computing device 204 may send a command to at least amagnetic element 212 to correct a position of rotor 216. In someembodiments, computing device 204 may perform a position stability checkof rotor 216 while rotor 216 has not come to a complete stop. In someembodiments, computing device 204 may send a command to at least amagnetic element 212 to rotate rotor 216 in a positive direction untilrotor 216 is in a stable position. In a non-limiting example, computingdevice 204 may detect rotor 216 may be in an unstable position whilerotating in a forward direction and providing lift to aircraft 208.Rather than work against a positive inertia by applying a negativetorque to rotor 216, computing device 204 may instead rotate rotor 216another portion of a rotation in a forward direction until rotor 216 isin a stable position. In some embodiments, computing device 204 may senda command to at least a magnetic element 212 to apply a positive torqueto rotor 216 until rotor 216 is in a stable position. In someembodiments, rotor 216 may rotate for a set period of time. In someembodiments, rotor 216 may rotate at a steady angular speed in order tolevel out a first end and a second end of rotor 216. In someembodiments, system 200 may include a plurality of halting points 220.

In some embodiments, and with continued reference to FIG. 2 , system 200may include a starting point 224. Starting point 224 may include a pointin which rotor 216 resumes movement. In some embodiments, starting point224 may include a point at which a halting of rotor 216 starts. In someembodiments, starting point 224 may be configured to prevent prematurehalting of rotor 216. Computing device 204 may search for starting point224 which may include, but is not limited to, half a complete rotation,a third of a complete rotation, a fourth of a complete rotation, or anyfraction of a complete rotation of rotor 216.

Now referring to FIG. 3 , an exemplary embodiment 300 of a flightcontroller 304 is illustrated. Flight controller 304 may include acomputing device 204 as described in FIG. 2 . As used in this disclosurea “flight controller” is a computing device of a plurality of computingdevices dedicated to data storage, security, distribution of traffic forload balancing, and flight instruction. Flight controller 304 mayinclude and/or communicate with any computing device as described inthis disclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 304may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 304 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 3 , flight controller 304may include a signal transformation component 308. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 308 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component308 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 308 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 308 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 308 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 3 , signal transformation component 308 may beconfigured to optimize an intermediate representation 312. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 308 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 308 may optimizeintermediate representation 312 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 308 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 308 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 304. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

Still referring to FIG. 3 , in an embodiment, and without limitation,signal transformation component 308 may include transform one or moreinputs and outputs as a function of an error correction ϕde. An errorcorrection ϕde, also known as error correcting code (ECC), is anencoding of a message or lot of data using redundant information,permitting recovery of corrupted data. An ECC may include a block code,in which information is encoded on fixed-size packets and/or blocks ofdata elements such as symbols of predetermined size, bits, or the like.Reed-Solomon coding, in which message symbols within a symbol set havingq symbols are encoded as coefficients of a polynomial of degree lessthan or equal to a natural number k, over a finite field F with qelements; strings so encoded have a minimum hamming distance of k+1, andpermit correction of (q-k−1)/2 erroneous symbols. Block code mayalternatively or additionally be implemented using Golay coding, alsoknown as binary Golay coding, Bose-Chaudhuri, Hocquenghuem (BCH) coding,multidimensional parity-check coding, and/or Hamming codes. An ECC mayalternatively or additionally be based on a convolutional code.

In an embodiment, and still referring to FIG. 3 , flight controller 304may include a reconfigurable hardware platform 316. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 316 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 3 , reconfigurable hardware platform 316 mayinclude a logic component 320. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 320 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 320 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 320 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 320 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 320 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 312. Logiccomponent 320 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 304. Logiccomponent 320 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 320 may beconfigured to execute the instruction on intermediate representation 312and/or output language. For example, and without limitation, logiccomponent 320 may be configured to execute an addition operation onintermediate representation 312 and/or output language.

In an embodiment, and without limitation, logic component 320 may beconfigured to calculate a flight element 324. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 324 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 324 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 324 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 3 , flight controller 304 may include a chipsetcomponent 328. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 328 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 320 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 328 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 320 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 328 maymanage data flow between logic component 320, memory cache, and a flightcomponent 332. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component 332 may include acomponent used to affect the aircrafts' roll and pitch which maycomprise one or more ailerons. As a further example, flight component332 may include a rudder to control yaw of an aircraft. In anembodiment, chipset component 328 may be configured to communicate witha plurality of flight components as a function of flight element 324.For example, and without limitation, chipset component 328 may transmitto an aircraft rotor to reduce torque of a first lift propulsor andincrease the forward thrust produced by a pusher component to perform aflight maneuver.

In an embodiment, and still referring to FIG. 3 , flight controller 304may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 304 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 324. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 304 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 304 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 3 , flight controller 304may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 324 and a pilot signal336 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 336may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 336 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 336may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 336 may include an explicitsignal directing flight controller 304 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 336 may include an implicit signal, wherein flight controller 304detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 336 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 336 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 336 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 336 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal336 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 3 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller 304 and/or a remote device may or may not use inthe generation of autonomous function. As used in this disclosure“remote device” is an external device to flight controller 304.Additionally or alternatively, autonomous machine-learning model mayinclude one or more autonomous machine-learning processes that afield-programmable gate array (FPGA) may or may not use in thegeneration of autonomous function. Autonomous machine-learning processmay include, without limitation machine learning processes such assimple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naïve bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

In an embodiment, and still referring to FIG. 3 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 304 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 3 , flight controller 304 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 304. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 304 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 304 as a software update,firmware update, or corrected habit machine-learning model. For example,and without limitation autonomous machine learning model may utilize aneural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 3 , flight controller 304 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 3 , flight controller 304may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller304 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 304 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 304 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Mass., USA. In an embodiment, and without limitation,control algorithm may be configured to generate an auto-code, wherein an“auto-code,” is used herein, is a code and/or algorithm that isgenerated as a function of the one or more models and/or software's. Inanother embodiment, control algorithm may be configured to produce asegmented control algorithm. As used in this disclosure a “segmentedcontrol algorithm” is control algorithm that has been separated and/orparsed into discrete sections. For example, and without limitation,segmented control algorithm may parse control algorithm into two or moresegments, wherein each segment of control algorithm may be performed byone or more flight controllers operating on distinct flight components.

In an embodiment, and still referring to FIG. 3 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 332. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 3 , the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 304. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 312 and/or output language from logiccomponent 320, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 3 , master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 3 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 3 , flight controller 304 may also beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofaircraft and/or computing device. Flight controller 304 may include adistributer flight controller. As used in this disclosure a “distributerflight controller” is a component that adjusts and/or controls aplurality of flight components as a function of a plurality of flightcontrollers. For example, distributer flight controller may include aflight controller that communicates with a plurality of additionalflight controllers and/or clusters of flight controllers. In anembodiment, distributed flight control may include one or more neuralnetworks. For example, neural network also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Still referring to FIG. 3 , a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function ϕ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 3 , flight controller may include asub-controller 340. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 304 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 340may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 340 may include any component of any flightcontroller as described above. Sub-controller 340 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 340may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 340 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 3 , flight controller may include aco-controller 344. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller 304 ascomponents and/or nodes of a distributer flight controller as describedabove. For example, and without limitation, co-controller 344 mayinclude one or more controllers and/or components that are similar toflight controller 304. As a further non-limiting example, co-controller344 may include any controller and/or component that joins flightcontroller 304 to distributer flight controller. As a furthernon-limiting example, co-controller 344 may include one or moreprocessors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data to and/or from flightcontroller 304 to distributed flight control system. Co-controller 344may include any component of any flight controller as described above.Co-controller 344 may be implemented in any manner suitable forimplementation of a flight controller as described above.

In an embodiment, and with continued reference to FIG. 3 , flightcontroller 304 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 304 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 4 , an exemplary embodiment of a machine-learningmodule 400 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module 400may be implemented in the determination of the flight states of theelectric aircraft. Machine-learning module 400 may communicated with theflight controller to determine a minimal drag axis for the electricaircraft. Machine-learning module may perform determinations,classification, and/or analysis steps, methods, processes, or the likeas described in this disclosure using machine learning processes. A“machine learning process,” as used in this disclosure, is a processthat automatedly uses training data 404 to generate an algorithm thatwill be performed by a computing device/module to produce outputs 408given data provided as inputs 412; this is in contrast to a non-machinelearning software program where the commands to be executed aredetermined in advance by a user and written in a programming language.

Still referring to FIG. 4 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 404 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 404 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 404 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 404 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 404 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 404 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data404 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 4 ,training data 404 may include one or more elements that are notcategorized; that is, training data 404 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 404 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 404 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 404 used by machine-learning module 400 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 4 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 416. Training data classifier 416 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 400 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 404. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 4 , machine-learning module 400 may beconfigured to perform a lazy-learning process 420 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 404. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 404 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naive Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 4 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 424. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 424 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 424 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 404set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 4 , machine-learning algorithms may include atleast a supervised machine-learning process 428. At least a supervisedmachine-learning process 428, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 404. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process428 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 4 , machine learning processes may include atleast an unsupervised machine-learning processes 432. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 4 , machine-learning module 400 may be designedand configured to create a machine-learning model 424 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 4 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 5 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 500 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 500 includes a processor 504 and a memory508 that communicate with each other, and with other components, via abus 512. Bus 512 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 504 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 504 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 504 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 508 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 516 (BIOS), including basic routines that help totransfer information between elements within computer system 500, suchas during start-up, may be stored in memory 508. Memory 508 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 520 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 508 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 500 may also include a storage device 524. Examples of astorage device (e.g., storage device 524) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 524 may be connected to bus 512 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 524 (or one or morecomponents thereof) may be removably interfaced with computer system 500(e.g., via an external port connector (not shown)). Particularly,storage device 524 and an associated machine-readable medium 528 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 500. In one example, software 520 may reside, completelyor partially, within machine-readable medium 528. In another example,software 520 may reside, completely or partially, within processor 504.

Computer system 500 may also include an input device 532. In oneexample, a user of computer system 500 may enter commands and/or otherinformation into computer system 500 via input device 532. Examples ofan input device 532 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 532may be interfaced to bus 512 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 512, and any combinations thereof. Input device 532 mayinclude a touch screen interface that may be a part of or separate fromdisplay 536, discussed further below. Input device 532 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 500 via storage device 524 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 540. A network interfacedevice, such as network interface device 540, may be utilized forconnecting computer system 500 to one or more of a variety of networks,such as network 544, and one or more remote devices 548 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 544,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 520,etc.) may be communicated to and/or from computer system 500 via networkinterface device 540.

Computer system 500 may further include a video display adapter 552 forcommunicating a displayable image to a display device, such as displaydevice 536. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 552 and display device 536 may be utilized incombination with processor 504 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 500 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 512 via a peripheral interface 556. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

Now referring to FIG. 6 , a method 600 for rotor management of anaircraft is presented. At step 605, a drag minimization axis of a rotoris determined. A drag minimization axis may include an axis across arotor in which air drag may be minimized. In a non-limiting example, adrag minimization axis may include a horizontal axis across a rotor thatmay allow a reduction of air resistance across the rotor. In someembodiments, a drag minimization axis may include a vertical axis acrossa rotor. In some embodiments, a drag minimization axis may include anoffset angle from an otherwise horizontal and/or vertical axis. In someembodiments, a plurality of sensor may be used to determine a dragminimization axis of a rotor. In some embodiments, a rotor may include avertical/and or horizontal rotor. A rotor may be connected to a vehicle.In some embodiments, a vehicle may include an aircraft. In someembodiments, a vehicle may include an electric aircraft, such as aneVTOL. A drag minimization axis may be determined through a feedbackloop of a plurality of sensors and a computing device. A dragminimization axis may continuously be updated, such as in real-time.

Still referring to FIG. 6 , at step 610, a halting point of the rotor isdetermined. A halting point may include a point in which a rotor maystop all motion. In some embodiments, a halting point may include a dragminimization axis. In some embodiments, a halting point may include astable position. A stable position may include a position in which afirst end and second end of a rotor are within an acceptable tilt anglerange. In some embodiments, a halting point may include a specific timeperiod of a rotation of a rotor. In other embodiments, a halting pointmay include a specific rotational angle of a rotor.

Still referring to FIG. 6 , at step 615, a halting command is sent to atleast a magnetic element to stop a movement of the rotor. At least amagnetic element may be configured to apply a torque to a rotor. In someembodiments, at least a magnetic element may be configured to apply apositive and/or negative torque to a rotor. In some embodiments, atleast a magnetic element may be configured to apply a torque to a rotoruntil the rotor is in a halting point. In some embodiments, at least amagnetic element may be configured to apply a net zero torque on arotor. In some embodiments, at least a magnetic element may continuallyadjust a torque applied to a rotor based on feedback from a plurality ofsensors of the rotor. In some embodiments, once a rotor is in a haltingpoint, a computing device may determine if the rotor is in a stableposition. In some embodiments, if it is determined that a rotor is notin a stable position, a command may be sent to at least a magneticelement to adjust the rotor until it is in a stable position.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

1. A system comprising: a computing device, wherein the computing deviceis configured to: determine a drag minimization axis of a rotorconnected to an aircraft, the rotor having a first end and a second end,using a machine learning module in communication with a flightcontroller, wherein the rotor is configured to rotate about an axis,wherein determining the drag minimization axis further comprisesdetermining the drag minimization axis of the rotor based on surroundingairflow of the aircraft during flight; determine a halting point of therotor using a machine learning model generated by the machine learningmodule, wherein the halting point includes the drag minimization axis ofthe rotor and the machine learning model comprises a trained machinelearning model trained by a first training data set; and send a haltingcommand to at least a magnetic element to halt the rotor, wherein thehalting command is configured to stop a movement of the rotor andposition the rotor at the halting point, and the at least a magneticelement is configured to apply a positive torque to the rotor tooperably move the rotor to a stable position for operation of theaircraft, and the at least magnetic element comprises at least aninverter configured to adjust a speed and torque of the rotor, whereinthe at least magnetic element comprises a tolerance range of parkingbetween negative two degrees and two degrees with respect to the stableposition, and wherein the halting command is further configured toposition the rotor to point towards a direction of the surroundingairflow in order to reduce drag; wherein the position of the rotor inthe halting point includes the first end pointing in one direction ofthe drag minimization axis and the second end pointing in an oppositedirection of the first end.
 2. The system of claim 1, whereindetermining a halting point of the rotor includes determining if therotor is in a stable position once the rotor has halted.
 3. The systemof claim 2, wherein if the rotor is determined to not be in a stableposition, the computing device is configured to send a command to the atleast a magnetic element to rotate the rotor in a direction of rotationuntil the rotor is in a stable position.
 4. The system of claim 1,wherein the computing device is further configured to: detect a torqueof the rotor; and send a command to the at least a magnetic element toapply a net zero torque to the rotor.
 5. The system of claim 1, whereinthe computing device is further configured to: determine a startingpoint of the rotor; determine a rotational angle of the rotor; and senda command to the at least a magnetic element to rotate the rotor to thestarting point based on the rotational angle of the rotor.
 6. The systemof claim 5, wherein the starting point of the rotor includes a point ofthe rotor to resume rotating after previously being halted.
 7. Thesystem of claim 1, wherein the computing device is further configured tocontinuously update the halting point of the rotor based on a feedbackof a sensor of the rotor.
 8. The system of claim 1, wherein thecomputing device is configured to utilize the machine learning model todetermine an optimal halting point.
 9. The system of claim 1, whereinthe computing device is configured to determine a starting point of therotor while the rotor is in the halted position.
 10. The system of claim1, wherein the computing device is configured to determine a torquethreshold, wherein the torque threshold is a torque amount needed torotate the rotor in a specific rate of rotation.
 11. The system of claim1, wherein the halting command is further configured to stop a movementof the rotor during a flight of the aircraft.
 12. A method comprising:determining, a drag minimization axis of a rotor connected to anaircraft using a machine learning module in communication with a flightcontroller, the rotor having a first end and a second end, wherein therotor is configured to rotate about an axis, wherein determining thedrag minimization axis further comprises determining the dragminimization axis of the rotor based on surrounding airflow of theaircraft during flight; determining, using a machine learning modelgenerated by the machine learning module, a halting point of the rotor,wherein the halting point includes the drag minimization axis of therotor and the first machine learning model comprises a trained firstmachine learning model trained by a first training data set; andsending, a halting command to at least a magnetic element to halt therotor, wherein the halting command is configured to stop a movement ofthe rotor and position the rotor in the halting point, wherein the atleast a magnetic element is configured to apply a positive torque to therotor to operably move the rotor to a stable position for operation ofthe aircraft and the at least magnetic element comprises at least aninverter configured to adjust a speed and torque of the rotor, whereinthe at least magnetic element comprises a tolerance range of parkingbetween negative two degrees and two degrees with respect to the stableposition, and wherein the halting command is further configured toposition the rotor to point towards a direction of the surroundingairflow in order to reduce drag; wherein the position of the rotor inthe halting point includes the first end pointing in one direction ofthe drag minimization axis and the second end pointing in an oppositedirection of the first end.
 13. The method of claim 12, whereindetermining a halting point of the rotor includes determining if therotor is in a stable position once the rotor has halted.
 14. The methodof claim 12, wherein if the rotor is determined to not be in a stableposition, the computing device is configured to send a command to the atleast a magnetic element to rotate the rotor in a direction of rotationuntil the rotor is in a stable position.
 15. The method of claim 12,wherein the computing device is further configured to: detect a torqueof the rotor; and send a command to the at least a magnetic element toapply a net zero torque to the rotor.
 16. The method of claim 12,wherein the computing device is further configured to: determine astarting point of the rotor; determine a rotational angle of the rotor;and send a command to the at least a magnetic element to rotate therotor to the starting point based on the rotational angle of the rotor.17. The method of claim 16, wherein the starting point of the rotorincludes a point of the rotor to resume rotating after previously beinghalted.
 18. The method of claim 12, wherein the computing device isfurther configured to continuously update the halting point of the rotorbased on a feedback of a sensor of the rotor.
 19. The method of claim12, wherein the computing device is configured to determine a torquethreshold, wherein the torque threshold is a torque amount needed torotate the rotor in a specific rate of rotation.
 20. The method of claim12, wherein the halting command is further configured to stop a movementof the rotor during a flight of the aircraft.